Paper Title
Utilizing Machine Learning for Efficient Detection of Hate Speech: An In-Depth Framework

Abstract
In the social platform the hate speech has become biggest problem, it's spreading a harmful ideas and also negativity. The large amount of content posted daily, it is very important to have a systems that can automatically detected and also moderatehate speech. The project focused in the building, The hate speech detection system using a machine learning and to identify and classify the hatespeechinthe text. we collected the dataset from online comments and prepared the text for the analysis to create this system and using a few techniques. We broke the text into Individual words (called tokenizatuon),and we removed commonwords like "the" or "and"( stop words),and we simplified some words to their base forms (lemmatization). Then we applied the machine learningmodels,likeLogisticRegressionandSupport Vector Machine (SVM),and also a Neural Networks, to classify whether the text is containing the hate speech or not. Regard help those models better understand the text,andwe used methods like TF- IDF (which measures how important a word is in a document) and also Word Embeddings (Word2Vec, GloVe) to capture the meaningful and the context of the words. Keywords - Harmfulideas,Negativity, Moderate, Identifyand classify, Tokenization, Lemmatization, Logistic Regression, Support Vector Machine (SVM), Neural Networks,TF-IDF, Word Embeddings, Word2Vec, GloVe.